Regarding the modules for the architecture including CNN backbone, RPN, and regression/classification heads, we follow the setting of RoITrans [1]}. The number of region proposals is set to 1,250. Based on the objects' uncertainties, we set top half nodes as source nodes while bottom half nodes as target nodes. If \(N_{nms}\)  is larger then 100, we only consider top 100 objects, and exclude the rest of highly uncertain objects that are likely to be noise. We set the dropout ratio as 0.2, \(M\)  for MC dropout as 50, \(\lambda _1, \lambda _2\)  for loss weights as 1, \(thr_{spa}\)  for sparse graph generation as 50, and \(thr_{sem}\)  for semantic graph generation as 10. The dimensionality of tensors in equation REF  is as follows: \(\hat{f}^{conv}_i \in \mathbb {R}^{256 \times 7 \times 7}\) , \(\hat{f}^{down}_i \in \mathbb {R}^{128 \times 7 \times 7}\) , and \(\hat{c}_i \in \mathbb {R}^{16 \times 7 \times 7}\) . For GNN layers, we use two GCN layers [2]} with LeakyReLU activation between them. Concatenated feature \(f^{node}_i\)  for each node is first fed into 1x1Conv for reducing its dimension from \(\mathbb {R}^{144 \times 7 \times 7}\)  to \(\mathbb {R}^{128 \times 7 \times 7}\) , and then fed into GCN, where its dimension is further reduced by 1/2 and 1/4 in two GCN layers, respectively, i.e., \(\hat{f}^{gnn}_i \in \mathbb {R}^{16 \times 7 \times 7}\) . We set a temperature scale value \(\tau \)  in \(\texttt {softmax}\)  of equation REF  as 0.1. The model is trained for 12 epochs with a batch size of 4, a learning rate of 0.01, and a weight decay of \(10^{-4}\) , and optimized by a SGD. We use 4 TITAN Xp GPUs.
